1. DIME / ITDG Meeting Luxemburg, 14 Feb 2017
Smart Statistics
DIME / ITDG Meeting
Luxemburg, 14 Feb 2017

2. In the era of real-time algorithmic decision making What is the place of Official Statistics? Issues? Challenges? Opportunities? Responsibilities?......?

3. From Big Data to Smart Statistics
Leveraging work on big data to develop cognitive statistics with algorithmic intelligence, automation, new standards, methods, ….., to meet the data4policy needs in a highly digital world

4. …transiting into Smart Statistics… this job is
Nearly Impossible
Challenging
Questionable
…….but also…..
Fascinating
Inspiring
Innovative
Motivating
Technically
Statistically
Ethically
Legally
Intellectually
Practically
…….
Necessary and Possible

5. New, dynamic data ecosystems
Massive increase of data production and exchange
Not manageable by humans
Exchange of data without human intervention
Autonomous Agents
Filtering, aggregation
Decisions
Cognitive systems
Can we use this new internet for producing statistics in an intelligent way?

6. Big Data
Most if not all data in a decade will be "organic"

7. Internet of Things / Wearable Internet
Sensors
Internet of Things / Wearable Internet
Machines communicate with machines and humans
Massive exchange of data & information
Intelligent systems to extract and aggregate data and transform to information
IoT will affect private life and economy
Intelligent home
Intelligent cities
Industry 4.0
Systems will take(semi)autonomous decisions

8. In order to realise the full benefits of AI, ‘things need to be connected first’

9. Artificial Intelligence & Machine Learning

10. The Cloud is a key component in Datafication

11. Internet of Things - Networks

12. Smart Statistics
We can think of Smart Statistics as being the future system of official statistics, where data capturing, processing and analysis will be embedded in the system itself, starting with the digital footprints of the activity.
putting intelligence to all stages of the data lifecycle it is expected to enhance the efficiency of the entire statistical system and enable the ESS to maintain and reinforce its role as a key provider of data4policy in a digital world

13. Smart Statistics - Approach
Dimensions
Architecture
What data is where?
Where is intelligence put?
Owner / operator
3rd party system, system deployed by statistical offices
Interactions
Between architecture, application domains, policy areas
Infrastructure for multiple purposes
Blending of traditional sources with smart statistics

14. Smart Statistics
Analysis of Architecture

15. Smart Systems Application Domain mobility cities homes wearables
energy grids
manufacturing
farming …

16. Smart Statistics4Policy
EU priorities
Jobs, Growth and Investment
Digital Single Market
Energy Union and Climate
Internal Market
A Deeper and Fairer Economic and Monetary Union
Migration
A Stronger Global Actor
Democratic change

17. Smart Statistics Contractor's proposal Deliverables
Potential policy areas,
application domains,
cross cutting issues (standards, software, networks, security, confidentiality, quality, methodology, …)
Deliverables
Use cases definition
Literature study as input
3 policy relevant use cases, small scale pilot
Generalize stat issues
Wider debate among stakeholders
Validate use case
Proof of concept with small scale piloting
Input to large scale piloting
Technical workshops

18. Smart Statistics: What next?
Planned Activities
Discussions with stakeholders (researchers, industry, …) on future Internet of Things
Feasibility Study 2017
Proof of Concept
Session in ISI World Statistics Congress 2017
More studies on selected use cases 2018+

19. Smart Statistics: Discussion
What are the basic components of a system(s) of smart statistics?
Which are the major statistical issues to address?
How should a 'Proof of Concept' look like in practice?
Do we need to develop new data and metadata standards together with IoT Standardisation bodies like ETSI?
Should we think of 'privacy by design' already at the development of a POC?
Whom we should partner with in this path towards smart statistics?
What could be the preferred scenario for implementation?
Do smart statistics represent new opportunities and challenges for citizen science and citizen data?
Can you propose some promising use cases for experimentation of smart statistics?
What are the most likely statistical domains for experimentation?

21. Building a European Data Economy
Communication adopted on 10 January 2017
Address issues on free flow of data
Emerging legal issues in context of new data technologies
access to and transfer of non-personal machine-generated data,
data liability, and
portability of non-personal data, interoperability and standards.

22. Building a European Data Economy
Access for public interest and scientific purposes
Public authorities could be granted access to data where this would be in the "general interest" and would considerably improve the functioning of the public sector …
EU needs a policy framework that enables data to be used throughout the value chain for scientific, societal and industrial purposes. To this end, the Commission is launching a wide-ranging stakeholder dialogue on the issues explored in this Communication. The first step in this dialogue will be a public consultation.

23. Building a European Data Economy
Use of commercially held data for official statistics
Re-use of smart meter information
Job vacancy statistics
Anonymised data from mobile phone operators
Ways forward
Non-legislative approaches
Guidance on incentivising business to share data
Fostering the development of technical solutions for reliable identification, exchange of, and differentiated access to data
Model contract terms
Legislative approaches
Default contractual rules
Access for public interest purposes
Data producer's right for non-personal or anonymised data
Access against remuneration to non-personal or anonymised data

24. Building a European Data Economy
Public Consultation
10 January – 26 April 2017
1) Localisation of data for storage and / or processing purposes
2) Access to and re-use of non-personal data
3) Liability
4) Portability of non-personal data, interoperability and standards